It is important to guide a user to interesting documents in a large-scale document-database. However, when the user is not an expert of the area of itnerest, it is difficult for him/her to name precise keywords in which he/she is interested, nor to select areas of his/her own interest. This paper presents an Index Navigator which clarifies what areas the user is interested in, what keywords he/she should investigate, and what documents concern his/her interest. A tough problem for such a system is to understand interesting areas for the user, among other areas-sets which can explain his/her behavior. Our Index Navigator employs an inference method called Cost-based Cooperation of Multi-abducers (CCMA), for understanding user's interest from the history of the user's expression of interest in incomplete keywords, even if the changing speed of the user's interest is totally unknown. with this device, the Index Navigator guides the user to really important areas, keywords and documents.
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